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We talk a lot about machine learning potentials, DFT surrogates, and foundation models for materials. But here’s a quiet truth: every new, truly predictive method still starts with an ab initio version.
ML potentials are getting shockingly good. But they depend on training data — and that data comes from the expensive, “ab initio version” codes. When the ab initio version changes (e.g., higher accuracy functional, core-valence correlation), the ML model’s ceiling moves too. ab initio versions
Real insight emerges when you know exactly what you’re approximating. Would you like this adapted for LinkedIn, Twitter, or a blog format? We talk a lot about machine learning potentials,
Here’s a draft for an interesting post about ab initio versions — tailored for a computational chemistry, materials science, or ML/physics audience. Why “Ab Initio” Versions Still Matter in an AI-Driven World But they depend on training data — and